# bitsandbytes

bitsandbytes is an open-source Python library for quantizing and running machine learning models with reduced precision on GPUs, widely used in deep learning and large language model inference and fine-tuning.

bitsandbytes is an open-source Python library that provides lightweight wrappers for quantizing and running [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) models with reduced numerical precision on graphics processing units (GPUs). It enables efficient memory usage and faster computation for [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) workloads, particularly for [large-language-model](https://www.wikiprompt.org/wiki/large-language-model) training, fine-tuning, and inference. The library is widely adopted in the [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) ecosystem, often integrated with popular frameworks such as Hugging Face Transformers and PyTorch.

The project originated from research on 8-bit optimizers and quantization techniques, first released in 2021 by Tim Dettmers, a researcher then at the University of Washington. It gained prominence as a practical tool for running large models on consumer hardware, reducing GPU memory requirements by representing weights and activations in lower precision formats, such as 8-bit integers (int8) and 4-bit floating-point (nf4).

## Core Functionality

bitsandbytes provides several key components. Its 8-bit optimizers, including Adam and SGD variants, reduce optimizer state memory during training. The library also offers quantization functions that convert model weights from 32-bit floating-point (fp32) to 8-bit or 4-bit formats, with dequantization for computation. A notable feature is the `LLM.int8()` method, which enables inference of large models like [transformer](https://www.wikiprompt.org/wiki/transformer)-based architectures with minimal performance degradation by using mixed-precision decomposition: outlier features are processed in fp16 while the majority of matrix multiplications use int8.

For 4-bit quantization, bitsandbytes implements the NormalFloat (NF4) data type, designed for normally distributed weights, and supports QLoRA (Quantized Low-Rank Adaptation), a technique for fine-tuning large models on a single GPU. QLoRA combines 4-bit base model quantization with low-rank adapters, allowing efficient parameter-efficient fine-tuning.

## Integration and Usage

The library integrates seamlessly with PyTorch, requiring minimal code changes. Users can load models in 8-bit or 4-bit precision via the `transformers` library by passing `load_in_8bit=True` or `load_in_4bit=True`. It also supports CPU offloading, enabling models larger than GPU memory to run by transferring layers to system RAM. bitsandbytes is compatible with NVIDIA GPUs with compute capability 7.5 or higher (e.g., Turing, Ampere, Ada Lovelace architectures).

As of 2024, the library supports a range of hardware, including recent [amd](https://www.wikiprompt.org/wiki/amd) GPUs through ROCm, and has been tested on [apple](https://www.wikiprompt.org/wiki/apple) silicon via Metal Performance Shaders in experimental builds. It is also used in cloud environments such as [amazon-web-services](https://www.wikiprompt.org/wiki/amazon-web-services), [azure](https://www.wikiprompt.org/wiki/azure), and [google-cloud](https://www.wikiprompt.org/wiki/google-cloud), where GPU instances are common.

## Impact on Model Deployment

bitsandbytes has lowered the barrier to running large models. For example, a 70-billion-parameter model that would require over 140 GB of memory in fp16 can be loaded in 4-bit precision using roughly 35 GB, fitting on a single high-end GPU like an NVIDIA A100 or RTX 4090. This capability has enabled researchers and hobbyists to experiment with state-of-the-art models without access to large clusters.

The library is a cornerstone of the open-source AI community, with thousands of projects on GitHub depending on it. It has been cited in numerous academic papers, including the QLoRA paper (2023), which demonstrated fine-tuning a 65-billion-parameter model on a single 48 GB GPU.

## Development and Community

bitsandbytes is maintained by a small team of contributors, with Tim Dettmers as the primary author. It is released under the MIT license, allowing free commercial and academic use. The project's development is supported by community contributions, and it is frequently updated to support new GPU architectures and quantization methods. As of 2025, the library has over 10,000 GitHub stars and is a standard tool in many [generative-ai](https://www.wikiprompt.org/wiki/generative-ai) pipelines.

## Limitations and Considerations

While quantization reduces memory, it can introduce slight accuracy loss, particularly for very large models or tasks requiring high numerical precision. The library's performance depends on GPU support; older GPUs without tensor core support may see slower speeds. Additionally, some operations, such as certain attention mechanisms, may not be fully optimized in quantized mode, requiring fallbacks to higher precision.

Despite these limitations, bitsandbytes remains a critical infrastructure piece for efficient AI, complementing other optimization techniques like pruning and distillation. Its continued evolution reflects the broader trend toward making [neural-network](https://www.wikiprompt.org/wiki/neural-network) models more accessible and sustainable.

## See Also

- [machine-learning](https://www.wikiprompt.org/wiki/machine-learning)
- [deep-learning](https://www.wikiprompt.org/wiki/deep-learning)
- [large-language-model](https://www.wikiprompt.org/wiki/large-language-model)
- [transformer](https://www.wikiprompt.org/wiki/transformer)
- [generative-ai](https://www.wikiprompt.org/wiki/generative-ai)

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Source: https://www.wikiprompt.org/wiki/bitsandbytes
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-07T02:31:37.812586+00:00